Big Data Mining

The course introduces the student to the main concepts of statistical analysis, the methods used and the software implementations to carry out a quantitative and rigorous study of a dataset. After introducing the basic tools of descriptive statistics, the course focuses on probabilistic statistics and its use for data modelling, estimation methods through an inferential approach and statistical hypothesis testing.

The course will deal with time series and spatio-temporal data, in particular mobility. We will illustrate the fundamental characteristics of these two data classes as well as the most common pre-processing and analysis methods. Finally, each lesson will provide examples of use and exercises carried out in Python with the appropriate libraries.

Prerequisites: Data Mining & Machine Learning, Python

Over the past decade, there has been a growing public fascination with the complex “connectedness” of modern society. This connectedness is found in many contexts: in the rapid growth of the Internet and the Web, in the ease with which global communication now takes place, and in the ability of news and information as well as epidemics and financial crises to spread around the world with surprising speed and intensity.

The module presents the methodological aspects, technologies and systems for designing predictive systems of Artificial Intelligence through machine learning and deep neural networks. The emphasis is placed on the analysis of application problems using examples and case studies, with practical exercises.

Prerequisites: Python & Data Mining & Machine Learning

The formidable advances in computing power, data acquisition, data storage and connectivity have created unprecedented amounts of data. Data mining, i.e., the science of extracting knowledge from these masses of data, has therefore been affirmed as an interdisciplinary branch of computer science. Data mining techniques have been applied to many industrial, scientific, and social problems, and are believed to have an ever deeper impact on society.

This module presents artificial intelligence techniques aimed at defining analytics on text and data from the Web. The course is organized around three main strands: i) text analytics, where text mining methods applied to texts and social media are studied; ii) sorting techniques through the application of "learning to rank" techniques which have the purpose of estimating the relevance of objects with respect to user requirements, iii) web mining techniques aimed at exploiting user usage data to improve quality of services.

The course presents the main web data analysis techniques. By using the query log of a real search engine as a case study, students are guided in the development of a set of methodologies for data analysis aimed at creating the knowledge base for building a recommendation system. Then, the course discusses how the same information can be used to optimize the ranking in Web services. To this regard, the course introduces the learning to rank techniques aimed at estimating the relevance of objects with respect to specific user information needs.

The module provides an introduction to base concepts of data mining and knowledge extraction process, introducing analytical models and algorithms for clustering, classification and pattern discovery, also referring Big Data sources.

This module introduces the main techniques for the analysis and mining of user based opinions on Big Data generated mainly from the web. Emphasis will be put on text mining methods applied to text originated on social media. Moreover, the module presents the main web data analysis techniques. By using the query log of a real search engine as a case study, students are guided in the development of a set of methodologies for data analysis aimed at creating the knowledge base for building a recommendation system.

The purpose of the course is to introduce the main techniques in data mining and machine learning (including deep learning approaches) for the analysis of temporal data, in particular for time series and spatio-temporal data related to human mobility. The presentation will be supported by several case studies developed with the SoBigData.eu Laboratory.